Codebook Offline Data - Not Shared

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)

library(codebook)
library(labelled)
#codebook_data <- codebook::bfi
# to import an SPSS file from the same folder uncomment and edit the line below
# codebook_data <- rio::import("mydata.sav")
# for Stata
# codebook_data <- rio::import("mydata.dta")
# for CSV
codebook_data <- rio::import("clean_offline_data_non-shared.csv")

# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
    only_labelled = TRUE, # only labelled values are autodetected as
                                   # missing
    negative_values_are_missing = FALSE, # negative values are missing values
    ninety_nine_problems = TRUE   # 99/999 are missing values, if they
                                   # are more than 5 MAD from the median
    )

# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
#codebook_data <- detect_scales(codebook_data)
# label variables correctly
#  

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

####   removing doubled column baby anchoring and scale mean scores, as the latter are created anyways  ####
drop <- c("anc_baby", "E", "A", "C", "N", "O", "soc_d_mean")
codebook_data = codebook_data[ , !(names(codebook_data) %in% drop)]
#codebook_data = codebook_data[ , codebook_data$consent == "A"]
var_label(codebook_data) <- list(
        gender = "Reported gender information",
        acad_status = "Academic status", 
        age = "Age Group", 
        refused = "Refused participation after consent",
        reason = "Reason for refusal", 
        consent = "Data sharing policy in consent", 
        bf_1 = "Big 5 Extraversion item 1", 
        bf_2 = "Big 5 Agreeableness item 2", 
        bf_3 = "Big 5 Conscientiousness item 3", 
        bf_4 = "Big 5 Neuroticism item 4", 
        bf_5 = "Big 5 Openness to experience item 5", 
        bf_6 = "Big 5 Extraversion item 6", 
        bf_7 = "Big 5 Agreeableness item 7", 
        bf_8 = "Big 5 Conscientiousness item 8", 
        bf_9 = "Big 5 Neuroticism item 9", 
        bf_10 = "Big 5 Openness to experience item 10", 
        bf_11 = "Big 5 Extraversion item 11", 
        bf_12 = "Big 5 Agreeableness item 12", 
        bf_13 = "Big 5 Conscientiousness item 13", 
        bf_14 = "Big 5 Neuroticism item 14", 
        bf_15 = "Big 5 Openness to experience item 15", 
        bf_16 = "Big 5 Extraversion item 16", 
        bf_17 = "Big 5 Agreeableness item 17", 
        bf_18 = "Big 5 Conscientiousness item 18", 
        bf_19 = "Big 5 Neuroticism item 19", 
        bf_20 = "Big 5 Openness to experience item 20", 
        bf_21 = "Big 5 Extraversion item 21", 
        bf_22 = "Big 5 Agreeableness item 22", 
        bf_23 = "Big 5 Conscientiousness item 23", 
        bf_24 = "Big 5 Neuroticism item 24", 
        bf_25 = "Big 5 Openness to experience item 25", 
        bf_26 = "Big 5 Extraversion item 26", 
        bf_27 = "Big 5 Agreeableness item 27", 
        bf_28 = "Big 5 Conscientiousness item 28", 
        bf_29 = "Big 5 Neuroticism item 29", 
        bf_30 = "Big 5 Openness to experience item 30", 
        bf_31 = "Big 5 Extraversion item 31", 
        bf_32 = "Big 5 Agreeableness item 32", 
        bf_33 = "Big 5 Conscientiousness item 33", 
        bf_34 = "Big 5 Neuroticism item 34", 
        bf_35 = "Big 5 Openness to experience item 35", 
        bf_36 = "Big 5 Extraversion item 36", 
        bf_37 = "Big 5 Agreeableness item 37", 
        bf_38 = "Big 5 Conscientiousness item 38", 
        bf_39 = "Big 5 Neuroticism item 39", 
        bf_40 = "Big 5 Openness to experience item 40", 
        bf_41 = "Big 5 Openness to experience item 41", 
        bf_42 = "Big 5 Agreeableness item 42", 
        bf_43 = "Big 5 Conscientiousness item 43", 
        bf_44 = "Big 5 Openness to experience item 44", 
        bf_45 = "Big 5 Agreeableness item 45", 
        cr_1 = "Careless response item 1", 
        cr_2 = "Careless response item 2", 
        cr_3 = "Careless response item 3", 
        cr_4 = "Careless response item 4", 
        cr_5 = "Careless response item 5", 
        cr_6 = "Careless response item 6", 
        cr_7 = "Careless response item 7", 
        soc_d_1 = "Social desirability item 1", 
        soc_d_2 = "Social desirability item 2", 
        soc_d_3 = "Social desirability item 3", 
        soc_d_4 = "Social desirability item 4", 
        soc_d_5 = "Social desirability item 5", 
        soc_d_6 = "Social desirability item 6", 
        soc_d_7 = "Social desirability item 7", 
        soc_d_8 = "Social desirability item 8", 
        soc_d_9 = "Social desirability item 9", 
        soc_d_10 = "Social desirability item 10", 
        soc_d_11 = "Social desirability item 11", 
        soc_d_12 = "Social desirability item 12", 
        soc_d_13 = "Social desirability item 13", 
        soc_d_14 = "Social desirability item 14", 
        soc_d_15 = "Social desirability item 15", 
        soc_d_16 = "Social desirability item 16", 
        soc_d_17 = "Social desirability item 17", 
        soc_d_18 = "Social desirability item 18", 
        cond_anc = "Anchoring condition", 
        anc_everest = "Anchoring: How high is Mount Everest", 
        anc_chicago = "Anchoring: Population of Chicagor", 
        anc_bebe = "Anchoring: Babies born in US", 
        mc_1 = "Manipulation check, question 1: 'Do you remember the consent you signed in the beginning?'", 
        mc_2 = "Manipulation check, question 2: 'Do you remember if the consent contained the topic of sharing anonymous data with others?' ", 
        mc_3 = "Manipulation check, question: 'Will your anonymous data be shared with others?'", 
        remarks = "Observer remarks"#, 
        # anc_baby", 
        # E = "Mean score on Extraversion subscale", 
        # A = "Mean score on Agreeableness subscale", 
        # C = "Mean score on Conscientiousness subscale", 
        # N = "Mean score on Neuroticism subscale", 
        # O = "Mean score on Openness to experience subscale", 
        # soc_d_mean = "Mean score on Social desirability scale"
)

val_labels(codebook_data$gender) <- c("Female" = 1, "Male" = 2)
val_labels(codebook_data$acad_status) <- c("other" = 0, "bachelor" = 1, "master" = 2, "PhD" = 3, "other" = 4)
val_labels(codebook_data$age) <- c("18-29 yo" = 1, "21-25 yo" = 2, "26-30 yo" = 3, "31-35 yo" = 4, "36-40 yo" = 5, "41-50 yo" = 6, "51-60 yo" = 7, "61-70 yo" = 8, "71-80 yo" = 9, "81-110 yo" = 10)
val_labels(codebook_data$refused) <- c("No" = 0, "Yes" = 1)
val_labels(codebook_data$consent) <- c("Data will be shared" = "A", "Data will not be shared" = "B")
val_labels(codebook_data$cond_anc) <- c("Low anchoring value" = 0, "High anchoring value" = 1)
val_labels(codebook_data$reason) <- c("Not refused" = 0, "Data sharing" = 1, "Not enough time" = 2, "Other" = 3)
val_labels(codebook_data$mc_1) <- c("Yes" = 1, "No" = 0)
val_labels(codebook_data$mc_2) <- c("Yes" = 1, "No" = 0)
val_labels(codebook_data$mc_3) <- c("Yes" = 1, "No" = 0)

add_likert_labels <- function(x) {
  val_labels(x) <- c("Disapprove strongly" = 1, 
                  "Disapprove slightly" = 2, 
                  "Neither approve nore disapprove" = 3,
                  "Approve slightly" = 4,
                  "Approve strongly" = 5)
  x
}
likert_items <- names(codebook_data[, c(10:79) ])
codebook_data <- codebook_data %>% mutate_at(likert_items, add_likert_labels)

####     Extraversion     ####
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_6", "bf_11", "bf_16", "bf_21", "bf_26", 
           "bf_31", "bf_36") %>% aggregate_and_document_scale()

reversed_items <- c("bf_6", "bf_21", "bf_31")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Extraversion <- codebook_data %>% select("bf_1", "bf_6R", "bf_11", "bf_16", "bf_21R", "bf_26", 
           "bf_31R", "bf_36") %>% aggregate_and_document_scale()

####    Agreeableness    ####
codebook_data$Agreeableness <- codebook_data %>% select("bf_2", "bf_7", "bf_12", "bf_17", "bf_22", 
           "bf_27", "bf_32", "bf_37", "bf_42", "bf_45") %>% aggregate_and_document_scale()

reversed_items <- c("bf_2", "bf_12", "bf_27", "bf_37", "bf_45")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Agreeableness <- codebook_data %>% select("bf_2R", "bf_7", "bf_12R", "bf_17", "bf_22", 
           "bf_27R", "bf_32", "bf_37R", "bf_42", "bf_45R") %>% aggregate_and_document_scale()

####    Conscientiousness    ####
codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_8","bf_13", "bf_18", "bf_23", "bf_28", 
           "bf_33", "bf_38", "bf_43") %>% aggregate_and_document_scale()

reversed_items <- c("bf_8", "bf_18", "bf_23", "bf_38", "bf_43")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Conscientiousness <- codebook_data %>% select("bf_3", "bf_8R","bf_13", "bf_18R", "bf_23R", "bf_28", 
           "bf_33", "bf_38R", "bf_43R") %>% aggregate_and_document_scale()

####    Neuroticism    ####
codebook_data$Neuroticism <- codebook_data %>% select("bf_4", "bf_9", "bf_14", "bf_19", "bf_24", "bf_29", 
           "bf_34", "bf_39") %>% aggregate_and_document_scale()

reversed_items <- c("bf_9", "bf_24", "bf_34")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$Neuroticism <- codebook_data %>% select("bf_4", "bf_9R", "bf_14", "bf_19", "bf_24R", "bf_29", 
           "bf_34R", "bf_39") %>% aggregate_and_document_scale()

####    Openness to experience    ####
codebook_data$'Openness to experience' <- codebook_data %>% select("bf_5", "bf_10", "bf_15", "bf_20", "bf_25", "bf_30", 
           "bf_35", "bf_40", "bf_41", "bf_44") %>% aggregate_and_document_scale()

reversed_items <- c("bf_35", "bf_41")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Openness to experience' <- codebook_data %>% select("bf_5", "bf_10", "bf_15", "bf_20", "bf_25", "bf_30", 
           "bf_35R", "bf_40", "bf_41R", "bf_44") %>% aggregate_and_document_scale()

####    Hétéroduperie/Social Desirability    ####
codebook_data$'Heteroduperie - Social Desirability' <- codebook_data %>% select("soc_d_1",
         "soc_d_2", "soc_d_3", "soc_d_4","soc_d_5", "soc_d_6",
         "soc_d_7", "soc_d_8", "soc_d_9", "soc_d_10",
         "soc_d_11", "soc_d_12", "soc_d_13", "soc_d_14", 
         "soc_d_15", "soc_d_16", "soc_d_17", "soc_d_18") %>% aggregate_and_document_scale()

reversed_items <- c("soc_d_2", "soc_d_4", "soc_d_5", "soc_d_7",
                    "soc_d_10", "soc_d_11", "soc_d_13", "soc_d_14",
                    "soc_d_17")

codebook_data <- codebook_data %>% 
  rename_at(reversed_items,  add_R)

codebook_data <- codebook_data %>% 
     mutate_at(vars(matches("\\dR$")), reverse_labelled_values)
 
codebook_data$'Heteroduperie - Social Desirability' <- codebook_data %>% select("soc_d_1",
         "soc_d_2R", "soc_d_3", "soc_d_4R", "soc_d_5R", 
         "soc_d_6", "soc_d_7R", "soc_d_8", "soc_d_9", 
         "soc_d_10R", "soc_d_11R", "soc_d_12", "soc_d_13R",
         "soc_d_14R", "soc_d_15", "soc_d_16", "soc_d_17R",
         "soc_d_18") %>% aggregate_and_document_scale()

metadata(codebook_data)$name <- "Non-Shared Condition: French-Belgian student data on Big 5, Social Desirability as measured by Heteroduperie and Anchoring Paradigm, public data set"
metadata(codebook_data)$description <- "45 items taking from the french translation of the Big 5 Personality questionnaire (Plaisant et al. 2010), 18 items from the subscale 'Hétéroduperie' of the french social desirability scale (Tournois et al., 2010) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This part of the dataset cannot be publicly shared, as the study consent stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors"
metadata(codebook_data)$identifier <- "https://dx.doi.org/10.17605/OSF.IO/X25D3"
metadata(codebook_data)$creator <- "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein"
metadata(codebook_data)$citation <- "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Impact of Data sharing: Data collected offline. https://doi.org/10.17605/OSF.IO/X25D3"
metadata(codebook_data)$datePublished <- "2019-08-05"
metadata(codebook_data)$temporalCoverage <- "2018-12-03 to 2018-12-17" 
metadata(codebook_data)$spatialCoverage <- "Campus Solbosch, Universite libre de Bruxelles, Brussels, Belgium" 

#rio::export(codebook_data, "offline_data_shared.rds")

codebook(codebook_data)

knitr::asis_output(data_info)

Metadata

Description

if (exists("name", meta)) {
  glue::glue(
    "__Dataset name__: {name}",
    .envir = meta)
}

Dataset name: Non-Shared Condition: French-Belgian student data on Big 5, Social Desirability as measured by Heteroduperie and Anchoring Paradigm, public data set

cat(description)

45 items taking from the french translation of the Big 5 Personality questionnaire (Plaisant et al. 2010), 18 items from the subscale ‘Hétéroduperie’ of the french social desirability scale (Tournois et al., 2010) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This part of the dataset cannot be publicly shared, as the study consent stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors

Metadata for search engines

• Temporal Coverage: 2018-12-03 to 2018-12-17
• Spatial Coverage: Campus Solbosch, Universite libre de Bruxelles, Brussels, Belgium

• Citation: Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Impact of Data sharing: Data collected offline. https://doi.org/10.17605/OSF.IO/X25D3

• Identifier: https://dx.doi.org/10.17605/OSF.IO/X25D3

• Date published: 2019-08-05

• Creator:Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein

meta <- meta[setdiff(names(meta),
                     c("creator", "datePublished", "identifier",
                       "url", "citation", "spatialCoverage", 
                       "temporalCoverage", "description", "name"))]
pander::pander(meta)

• keywords: V1, id, participant, gender, acad_status, age, refused, reason, consent, bf_1, bf_2R, bf_3, bf_4, bf_5, bf_6R, bf_7, bf_8R, bf_9R, bf_10, bf_11, bf_12R, bf_13, bf_14, bf_15, bf_16, bf_17, bf_18R, bf_19, bf_20, bf_21R, bf_22, bf_23R, bf_24R, bf_25, bf_26, bf_27R, bf_28, bf_29, bf_30, bf_31R, bf_32, bf_33, bf_34R, bf_35R, bf_36, bf_37R, bf_38R, bf_39, bf_40, bf_41R, bf_42, bf_43R, bf_44, bf_45R, cr_1, cr_2, cr_3, cr_4, cr_5, cr_6, cr_7, soc_d_1, soc_d_2R, soc_d_3, soc_d_4R, soc_d_5R, soc_d_6, soc_d_7R, soc_d_8, soc_d_9, soc_d_10R, soc_d_11R, soc_d_12, soc_d_13R, soc_d_14R, soc_d_15, soc_d_16, soc_d_17R, soc_d_18, cond_anc, anc_everest, anc_chicago, anc_bebe, mc_1, mc_2, mc_3, remarks, Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness to experience and Heteroduperie - Social Desirability

knitr::asis_output(survey_overview)

Variables

if (detailed_variables || detailed_scales) {
  knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}

V1

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
V1	integer	0	99	99	96.11	54.16	1	54.5	93	142	192	▅▆▆▇▆▆▆▆

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

id

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	empty	n_unique	min	max
id	character	0	99	99	0	99	4	5

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

participant

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	data_type	missing	complete	n	empty	n_unique	min	max
participant	character	0	99	99	0	99	4	5

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

gender

Reported gender information

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
gender	Reported gender information	integer	1. Female, 2. Male	0	99	99	1.38	0.49	1	1	1	2	2	▇▁▁▁▁▁▁▅

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Female: 1
• Male: 2

acad_status

Academic status

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
acad_status	Academic status	integer	0. other, 1. bachelor, 2. master, 3. PhD, 4. other	0	99	99	1.17	0.54	0	1	1	1	3	▁▁▇▁▁▂▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• other: 0
• bachelor: 1
• master: 2
• PhD: 3
• other: 4

age

Age Group

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
age	Age Group	integer	1. 18-29 yo, 2. 21-25 yo, 3. 26-30 yo, 4. 31-35 yo, 5. 36-40 yo, 6. 41-50 yo, 7. 51-60 yo, 8. 61-70 yo, 9. 71-80 yo, 10. 81-110 yo	0	99	99	1.66	0.93	1	1	1	2	7	▇▅▂▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• 18-29 yo: 1
• 21-25 yo: 2
• 26-30 yo: 3
• 31-35 yo: 4
• 36-40 yo: 5
• 41-50 yo: 6
• 51-60 yo: 7
• 61-70 yo: 8
• 71-80 yo: 9
• 81-110 yo: 10

refused

Refused participation after consent

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
refused	Refused participation after consent	integer	0. No, 1. Yes	0	99	99	0	0	0	0	0	0	0	▁▁▁▇▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• No: 0
• Yes: 1

reason

Reason for refusal

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
reason	Reason for refusal	integer	0. Not refused, 1. Data sharing, 2. Not enough time, 3. Other	0	99	99	0	0	0	0	0	0	0	▁▁▁▇▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Not refused: 0
• Data sharing: 1
• Not enough time: 2
• Other: 3

consent

Data sharing policy in consent

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	empty	n_unique	min	max
consent	Data sharing policy in consent	character	A. Data will be shared, B. Data will not be shared	0	99	99	0	1	1	1

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Data will be shared: A
• Data will not be shared: B

cr_1

Careless response item 1

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_1	Careless response item 1	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	1.25	0.58	1	1	1	1	4	▇▁▂▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_2

Careless response item 2

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

2 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_2	Careless response item 2	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	97	99	1.68	1.11	1	1	1	2	5	▇▁▁▂▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_3

Careless response item 3

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_3	Careless response item 3	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	1.74	1.13	1	1	1	2.75	5	▇▂▁▂▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_4

Careless response item 4

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_4	Careless response item 4	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	1.22	0.65	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_5

Careless response item 5

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_5	Careless response item 5	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	1.17	0.66	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_6

Careless response item 6

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_6	Careless response item 6	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	1.72	1.42	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cr_7

Careless response item 7

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cr_7	Careless response item 7	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	1.64	1.34	1	1	1	1	5	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Disapprove strongly: 1
• Disapprove slightly: 2
• Neither approve nore disapprove: 3
• Approve slightly: 4
• Approve strongly: 5

cond_anc

Anchoring condition

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
cond_anc	Anchoring condition	integer	0. Low anchoring value, 1. High anchoring value	0	99	99	0.51	0.5	0	0	1	1	1	▇▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Low anchoring value: 0
• High anchoring value: 1

anc_everest

Anchoring: How high is Mount Everest

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

5 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_everest	Anchoring: How high is Mount Everest	integer	5	94	99	7691.74	5263.54	600	2850	8000	10000	30000	▇▃▇▅▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

anc_chicago

Anchoring: Population of Chicagor

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

6 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_chicago	Anchoring: Population of Chicagor	numeric	6	93	99	34.12	258.87	0.004	1.5	3	6	2500	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

anc_bebe

Anchoring: Babies born in US

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

8 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
anc_bebe	Anchoring: Babies born in US	integer	8	91	99	74118.87	167458.67	80	1250	15000	50500	1e+06	▇▁▁▁▁▁▁▁

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

mc_1

Manipulation check, question 1: ‘Do you remember the consent you signed in the beginning?’

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
mc_1	Manipulation check, question 1: ‘Do you remember the consent you signed in the beginning?’	integer	1. Yes, 0. No	0	99	99	0.97	0.17	0	1	1	1	1	▁▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Yes: 1
• No: 0

mc_2

Manipulation check, question 2: ‘Do you remember if the consent contained the topic of sharing anonymous data with others?’

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
mc_2	Manipulation check, question 2: ‘Do you remember if the consent contained the topic of sharing anonymous data with others?’	integer	1. Yes, 0. No	0	99	99	0.66	0.48	0	0	1	1	1	▅▁▁▁▁▁▁▇

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Yes: 1
• No: 0

mc_3

Manipulation check, question: ‘Will your anonymous data be shared with others?’

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

1 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
mc_3	Manipulation check, question: ‘Will your anonymous data be shared with others?’	integer	1. Yes, 0. No	1	98	99	0.33	0.47	0	0	0	1	1	▇▁▁▁▁▁▁▃

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

Value labels

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

• Yes: 1
• No: 0

remarks

Observer remarks

Distribution

show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

    if (is.null(choices) || 
        dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
        non_missing_choices <- unique(item_nomiss)
        names(non_missing_choices) <- non_missing_choices
    }
  choice_multiplier <- old_height/6.5
    new_height <- 2 + choice_multiplier * length(non_missing_choices)
    new_height <- ifelse(new_height > 20, 20, new_height)
    new_height <- ifelse(new_height < 1, 1, new_height)
    if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
      new_height <- old_height
    }
    knitr::opts_chunk$set(fig.height = new_height)
}

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
      cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

knitr::opts_chunk$set(fig.height = old_height)

0 missing values.

Summary statistics

attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))

name	label	data_type	missing	complete	n	empty	n_unique	min	max
remarks	Observer remarks	character	0	99	99	86	14	0	69

if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (!is.null(choices) && !is.null(item_info$choices) && 
    all(names(na.omit(choices)) == item_info$choices) &&
    all(na.omit(choices) == names(item_info$choices))) {
    item_info$choices <- NULL
  }
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  pander::pander(item_info)
}

if (!is.null(choices) && length(choices) && length(choices) < 30) {
    pander::pander(as.list(choices))
}

Scale: Extraversion

Overview

Reliability: ω_ordinal [95% CI] = 0.21 [0.06;0.35].

Missing: 3.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.1009
Omega Psych Tot	0.9224
Omega Psych H	0.8314
Omega Ordinal	0.2053
Cronbach Alpha	-0.742
Greatest Lower Bound	0.1801
Alpha Ordinal	-0.3471

Positive correlations: 13 out of 28 (46%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_1, bf_6R, bf_11, bf_16, bf_21R, bf_26, bf_31R, bf_36
##               Observations: 96
##      Positive correlations: 13 out of 28 (46%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.1
##       Omega (hierarchical): 0.83
##    Revelle's omega (total): 0.92
## Greatest Lower Bound (GLB): 0.18
##              Coefficient H: 0.93
##           Cronbach's alpha: -0.74
## Confidence intervals:
##              Omega (total): [0, 0.22]
##           Cronbach's alpha: [0, -0.3]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.21
##  Ordinal Omega (hierarch.): 0.15
##   Ordinal Cronbach's alpha: -0.35
## Confidence intervals:
##      Ordinal Omega (total): [0.06, 0.35]
##   Ordinal Cronbach's alpha: [0, 0.07]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 4.818, 0.754, 0.665, 0.55, 0.456, 0.323, 0.232, 0.201
## Loadings:
##        PC1   
## bf_1    0.755
## bf_6R  -0.808
## bf_11   0.603
## bf_16   0.654
## bf_21R -0.868
## bf_26   0.790
## bf_31R -0.799
## bf_36   0.888
## 
##                  PC1
## SS loadings    4.818
## Proportion Var 0.602
## 
##        vars  n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_1      1 96 3.50 1.16      4    3.56 1.48   1   5     4 -0.36    -0.79
## bf_6R     2 96 3.06 1.41      3    3.08 1.48   1   5     4 -0.15    -1.33
## bf_11     3 96 3.66 1.04      4    3.73 1.48   1   5     4 -0.55    -0.33
## bf_16     4 96 3.66 1.16      4    3.74 1.48   1   5     4 -0.56    -0.69
## bf_21R    5 96 2.79 1.41      3    2.74 1.48   1   5     4  0.03    -1.35
## bf_26     6 96 3.42 1.18      4    3.47 1.48   1   5     4 -0.34    -0.84
## bf_31R    7 96 2.96 1.34      3    2.95 1.48   1   5     4 -0.13    -1.22
## bf_36     8 96 3.26 1.29      3    3.32 1.48   1   5     4 -0.25    -1.07
##          se
## bf_1   0.12
## bf_6R  0.14
## bf_11  0.11
## bf_16  0.12
## bf_21R 0.14
## bf_26  0.12
## bf_31R 0.14
## bf_36  0.13

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_1	Big 5 Extraversion item 1	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.55	1.17	1	3	4	4.5	5	▂▅▁▇▁▇▁▇
bf_6R	Big 5 Extraversion item 6	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.03	1.4	1	2	3	4	5	▆▅▁▅▁▇▁▅
bf_11	Big 5 Extraversion item 11	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.7	1.05	1	3	4	4	5	▁▂▁▅▁▇▁▅
bf_16	Big 5 Extraversion item 16	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.68	1.16	1	3	4	5	5	▁▃▁▃▁▇▁▆
bf_21R	Big 5 Extraversion item 21	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	2	97	99	2.8	1.4	1	1	3	4	5	▇▃▁▆▁▇▁▃
bf_26	Big 5 Extraversion item 26	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.42	1.16	1	3	4	4	5	▂▅▁▆▁▇▁▅
bf_31R	Big 5 Extraversion item 31	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	2.99	1.34	1	2	3	4	5	▆▅▁▆▁▇▁▃
bf_36	Big 5 Extraversion item 36	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.28	1.29	1	2	3	4	5	▃▅▁▆▁▇▁▆

Scale: Agreeableness

Overview

Reliability: ω_ordinal [95% CI] = 0.8 [0.74;0.86].

Missing: 2.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.7413
Omega Psych Tot	0.8053
Omega Psych H	0.4117
Omega Ordinal	0.7972
Cronbach Alpha	0.7449
Greatest Lower Bound	0.8228
Alpha Ordinal	0.7946

Positive correlations: 45 out of 45 (100%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_2R, bf_7, bf_12R, bf_17, bf_22, bf_27R, bf_32, bf_37R, bf_42, bf_45R
##               Observations: 97
##      Positive correlations: 45 out of 45 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.74
##       Omega (hierarchical): 0.41
##    Revelle's omega (total): 0.81
## Greatest Lower Bound (GLB): 0.82
##              Coefficient H: 0.78
##           Cronbach's alpha: 0.74
## Confidence intervals:
##              Omega (total): [0.66, 0.82]
##           Cronbach's alpha: [0.67, 0.82]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.8
##  Ordinal Omega (hierarch.): 0.79
##   Ordinal Cronbach's alpha: 0.79
## Confidence intervals:
##      Ordinal Omega (total): [0.74, 0.86]
##   Ordinal Cronbach's alpha: [0.73, 0.86]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.14, 1.6, 0.973, 0.835, 0.824, 0.678, 0.637, 0.475, 0.455, 0.381
## Loadings:
##        TC1    TC2   
## bf_2R          0.602
## bf_7    0.778       
## bf_12R         0.810
## bf_17   0.312  0.327
## bf_22   0.576       
## bf_27R         0.702
## bf_32   0.704  0.159
## bf_37R  0.612  0.198
## bf_42   0.739 -0.205
## bf_45R         0.679
## 
##                  TC1   TC2
## SS loadings    2.461 2.188
## Proportion Var 0.246 0.219
## Cumulative Var 0.246 0.465
## 
##        vars  n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_2R     1 97 3.61 1.12      4    3.68 1.48   1   5     4 -0.51    -0.61
## bf_7      2 97 4.18 0.91      4    4.29 1.48   2   5     3 -0.83    -0.28
## bf_12R    3 97 4.00 1.17      4    4.16 1.48   1   5     4 -1.00    -0.06
## bf_17     4 97 3.52 1.03      4    3.54 1.48   1   5     4 -0.41    -0.71
## bf_22     5 97 3.41 1.21      4    3.51 1.48   1   5     4 -0.54    -0.73
## bf_27R    6 97 4.03 1.11      4    4.18 1.48   1   5     4 -0.87    -0.33
## bf_32     7 97 3.91 0.97      4    4.03 1.48   1   5     4 -0.90     0.47
## bf_37R    8 97 4.04 1.07      4    4.18 1.48   1   5     4 -0.84    -0.41
## bf_42     9 97 3.71 1.15      4    3.82 1.48   1   5     4 -0.68    -0.41
## bf_45R   10 97 4.36 0.93      5    4.53 0.00   1   5     4 -1.46     1.53
##          se
## bf_2R  0.11
## bf_7   0.09
## bf_12R 0.12
## bf_17  0.10
## bf_22  0.12
## bf_27R 0.11
## bf_32  0.10
## bf_37R 0.11
## bf_42  0.12
## bf_45R 0.09

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_2R	Big 5 Agreeableness item 2	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	3.6	1.12	1	3	4	4	5	▁▃▁▅▁▇▁▅
bf_7	Big 5 Agreeableness item 7	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	4.17	0.9	2	4	4	5	5	▁▁▃▁▁▆▁▇
bf_12R	Big 5 Agreeableness item 12	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	3.96	1.2	1	3	4	5	5	▁▂▁▂▁▅▁▇
bf_17	Big 5 Agreeableness item 17	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.49	1.06	1	3	4	4	5	▁▃▁▃▁▇▁▃
bf_22	Big 5 Agreeableness item 22	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.41	1.21	1	3	4	4	5	▂▃▁▃▁▇▁▃
bf_27R	Big 5 Agreeableness item 27	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	4	1.12	1	3	4	5	5	▁▂▁▃▁▅▁▇
bf_32	Big 5 Agreeableness item 32	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.9	0.97	1	3.25	4	5	5	▁▂▁▂▁▇▁▅
bf_37R	Big 5 Agreeableness item 37	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	4.03	1.08	1	3	4	5	5	▁▂▁▂▁▅▁▇
bf_42	Big 5 Agreeableness item 42	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.69	1.17	1	3	4	5	5	▂▂▁▅▁▇▁▆
bf_45R	Big 5 Agreeableness item 45	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	4.36	0.92	1	4	5	5	5	▁▁▁▁▁▃▁▇

Scale: Conscientiousness

Overview

Reliability: ω_ordinal [95% CI] = 0.23 [0.08;0.38].

Missing: 2.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.1577
Omega Psych Tot	0.8571
Omega Psych H	0.5402
Omega Ordinal	0.2275
Cronbach Alpha	0.322
Greatest Lower Bound	0.6847
Alpha Ordinal	0.3773

Positive correlations: 16 out of 36 (44%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_3, bf_8R, bf_13, bf_18R, bf_23R, bf_28, bf_33, bf_38R, bf_43R
##               Observations: 97
##      Positive correlations: 16 out of 36 (44%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.16
##       Omega (hierarchical): 0.54
##    Revelle's omega (total): 0.86
## Greatest Lower Bound (GLB): 0.68
##              Coefficient H: 0.85
##           Cronbach's alpha: 0.32
## Confidence intervals:
##              Omega (total): [0, 0.32]
##           Cronbach's alpha: [0.13, 0.52]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.23
##  Ordinal Omega (hierarch.): 0.13
##   Ordinal Cronbach's alpha: 0.38
## Confidence intervals:
##      Ordinal Omega (total): [0.08, 0.38]
##   Ordinal Cronbach's alpha: [0.18, 0.57]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.586, 1.518, 0.881, 0.698, 0.669, 0.513, 0.492, 0.378, 0.266
## Loadings:
##        TC1    TC2   
## bf_3    0.573 -0.376
## bf_8R          0.811
## bf_13   0.809  0.261
## bf_18R         0.683
## bf_23R         0.708
## bf_28   0.785       
## bf_33   0.849       
## bf_38R  0.560 -0.140
## bf_43R         0.660
## 
##                  TC1   TC2
## SS loadings    2.644 2.302
## Proportion Var 0.294 0.256
## Cumulative Var 0.294 0.550
## 
##        vars  n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_3      1 97 3.53 1.24      4    3.63 1.48   1   5     4 -0.48    -0.76
## bf_8R     2 97 2.82 1.23      3    2.80 1.48   1   5     4  0.07    -1.13
## bf_13     3 97 3.97 0.98      4    4.06 1.48   1   5     4 -0.72     0.04
## bf_18R    4 97 2.77 1.33      3    2.72 1.48   1   5     4  0.04    -1.21
## bf_23R    5 97 3.05 1.25      3    3.06 1.48   1   5     4  0.00    -1.02
## bf_28     6 97 3.81 1.10      4    3.92 1.48   1   5     4 -0.74    -0.32
## bf_33     7 97 3.78 0.93      4    3.87 1.48   1   5     4 -0.73     0.39
## bf_38R    8 97 3.60 1.07      4    3.68 1.48   1   5     4 -0.63    -0.11
## bf_43R    9 97 3.40 1.37      4    3.49 1.48   1   5     4 -0.52    -0.98
##          se
## bf_3   0.13
## bf_8R  0.13
## bf_13  0.10
## bf_18R 0.13
## bf_23R 0.13
## bf_28  0.11
## bf_33  0.09
## bf_38R 0.11
## bf_43R 0.14

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_3	Big 5 Conscientiousness item 3	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.52	1.24	1	3	4	5	5	▂▃▁▇▁▇▁▇
bf_8R	Big 5 Conscientiousness item 8	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	2.85	1.24	1	2	3	4	5	▅▇▁▆▁▇▁▂
bf_13	Big 5 Conscientiousness item 13	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.97	0.98	1	3	4	5	5	▁▁▁▆▁▇▁▇
bf_18R	Big 5 Conscientiousness item 18	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	2.8	1.34	1	2	3	4	5	▇▅▁▇▁▇▁▃
bf_23R	Big 5 Conscientiousness item 23	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.07	1.26	1	2	3	4	5	▃▆▁▇▁▆▁▅
bf_28	Big 5 Conscientiousness item 28	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.83	1.1	1	3	4	5	5	▁▂▁▃▁▇▁▇
bf_33	Big 5 Conscientiousness item 33	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.77	0.93	1	3	4	4	5	▁▁▁▃▁▇▁▃
bf_38R	Big 5 Conscientiousness item 38	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.6	1.08	1	3	4	4	5	▁▂▁▅▁▇▁▅
bf_43R	Big 5 Conscientiousness item 43	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.41	1.37	1	2.5	4	4.5	5	▃▂▁▅▁▇▁▆

Scale: Neuroticism

Overview

Reliability: ω_ordinal [95% CI] = 0.85 [0.8;0.89].

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.8357
Omega Psych Tot	0.873
Omega Psych H	0.7442
Omega Ordinal	0.8467
Cronbach Alpha	0.8319
Greatest Lower Bound	0.8607
Alpha Ordinal	0.8428

Positive correlations: 28 out of 28 (100%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_4, bf_9R, bf_14, bf_19, bf_24R, bf_29, bf_34R, bf_39
##               Observations: 98
##      Positive correlations: 28 out of 28 (100%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.84
##       Omega (hierarchical): 0.74
##    Revelle's omega (total): 0.87
## Greatest Lower Bound (GLB): 0.86
##              Coefficient H: 0.88
##           Cronbach's alpha: 0.83
## Confidence intervals:
##              Omega (total): [0.79, 0.88]
##           Cronbach's alpha: [0.78, 0.88]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.85
##  Ordinal Omega (hierarch.): 0.83
##   Ordinal Cronbach's alpha: 0.84
## Confidence intervals:
##      Ordinal Omega (total): [0.8, 0.89]
##   Ordinal Cronbach's alpha: [0.8, 0.89]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.779, 1.07, 0.8, 0.697, 0.572, 0.45, 0.364, 0.266
## Loadings:
##        TC1    TC2   
## bf_4           0.776
## bf_9R   0.718  0.198
## bf_14   0.362  0.545
## bf_19   0.199  0.675
## bf_24R  0.690       
## bf_29  -0.181  0.778
## bf_34R  0.879 -0.121
## bf_39   0.548  0.468
## 
##                  TC1   TC2
## SS loadings    2.270 2.237
## Proportion Var 0.284 0.280
## Cumulative Var 0.284 0.563
## 
##        vars  n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_4      1 98 2.03 1.23      2    1.85 1.48   1   5     4  0.99    -0.10
## bf_9R     2 98 3.15 1.27      3    3.19 1.48   1   5     4 -0.02    -1.10
## bf_14     3 98 3.14 1.32      3    3.17 1.48   1   5     4 -0.23    -1.21
## bf_19     4 98 3.19 1.30      3    3.24 1.48   1   5     4 -0.25    -1.09
## bf_24R    5 98 3.13 1.30      3    3.16 1.48   1   5     4 -0.05    -1.11
## bf_29     6 98 2.96 1.39      3    2.95 1.48   1   5     4 -0.02    -1.32
## bf_34R    7 98 2.88 1.29      3    2.85 1.48   1   5     4  0.11    -1.11
## bf_39     8 98 2.87 1.29      3    2.84 1.48   1   5     4  0.13    -1.17
##          se
## bf_4   0.12
## bf_9R  0.13
## bf_14  0.13
## bf_19  0.13
## bf_24R 0.13
## bf_29  0.14
## bf_34R 0.13
## bf_39  0.13

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_4	Big 5 Neuroticism item 4	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	2.02	1.23	1	1	2	3	5	▇▃▁▂▁▂▁▁
bf_9R	Big 5 Neuroticism item 9	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	3.13	1.28	1	2	3	4	5	▃▇▁▇▁▆▁▆
bf_14	Big 5 Neuroticism item 14	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.14	1.32	1	2	3	4	5	▃▅▁▃▁▇▁▃
bf_19	Big 5 Neuroticism item 19	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.19	1.3	1	2	3	4	5	▃▅▁▆▁▇▁▅
bf_24R	Big 5 Neuroticism item 24	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	98	99	3.13	1.3	1	2	3	4	5	▃▆▁▇▁▆▁▆
bf_29	Big 5 Neuroticism item 29	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	2.94	1.4	1	2	3	4	5	▇▇▁▆▁▇▁▅
bf_34R	Big 5 Neuroticism item 34	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	2.87	1.29	1	2	3	4	5	▆▇▁▇▁▆▁▅
bf_39	Big 5 Neuroticism item 39	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	2.85	1.3	1	2	3	4	5	▅▇▁▆▁▆▁▃

Scale: Openness to experience

Overview

Reliability: ω_ordinal [95% CI] = 0.69 [0.59;0.78].

Missing: 1.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.661
Omega Psych Tot	0.7831
Omega Psych H	0.5201
Omega Ordinal	0.6851
Cronbach Alpha	0.6188
Greatest Lower Bound	0.8336
Alpha Ordinal	0.6484

Positive correlations: 32 out of 45 (71%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: bf_5, bf_10, bf_15, bf_20, bf_25, bf_30, bf_35R, bf_40, bf_41R, bf_44
##               Observations: 98
##      Positive correlations: 32 out of 45 (71%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.66
##       Omega (hierarchical): 0.52
##    Revelle's omega (total): 0.78
## Greatest Lower Bound (GLB): 0.83
##              Coefficient H: 0.85
##           Cronbach's alpha: 0.62
## Confidence intervals:
##              Omega (total): [0.56, 0.76]
##           Cronbach's alpha: [0.52, 0.72]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.69
##  Ordinal Omega (hierarch.): 0.67
##   Ordinal Cronbach's alpha: 0.65
## Confidence intervals:
##      Ordinal Omega (total): [0.59, 0.78]
##   Ordinal Cronbach's alpha: [0.55, 0.75]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 3.212, 1.321, 1.191, 1.156, 0.914, 0.61, 0.584, 0.427, 0.333, 0.252
## Loadings:
##        TC1    TC2    TC3    TC4   
## bf_5    0.725         0.127 -0.116
## bf_10   0.152  0.744  0.218 -0.102
## bf_15                        0.955
## bf_20   0.841         0.152       
## bf_25   0.756  0.106         0.147
## bf_30   0.763        -0.346 -0.306
## bf_35R  0.194 -0.778  0.332       
## bf_40   0.359  0.580  0.171  0.211
## bf_41R                0.901       
## bf_44   0.558        -0.246  0.221
## 
##                  TC1   TC2   TC3   TC4
## SS loadings    2.888 1.520 1.224 1.162
## Proportion Var 0.289 0.152 0.122 0.116
## Cumulative Var 0.289 0.441 0.563 0.679
## 
##        vars  n mean   sd median trimmed  mad min max range  skew kurtosis
## bf_5      1 98 3.47 0.97      4    3.50 1.48   1   5     4 -0.42    -0.20
## bf_10     2 98 4.07 0.89      4    4.17 1.48   1   5     4 -1.10     1.53
## bf_15     3 98 2.90 0.91      3    2.90 0.00   1   5     4  0.04     0.25
## bf_20     4 98 3.68 0.99      4    3.75 1.48   1   5     4 -0.48    -0.32
## bf_25     5 98 3.54 0.95      4    3.60 1.48   1   5     4 -0.68     0.33
## bf_30     6 98 3.61 1.30      4    3.75 1.48   1   5     4 -0.61    -0.77
## bf_35R    7 98 2.30 1.14      2    2.19 1.48   1   5     4  0.69    -0.35
## bf_40     8 98 3.88 0.89      4    3.95 1.48   1   5     4 -0.64     0.18
## bf_41R    9 98 2.52 1.47      2    2.41 1.48   1   5     4  0.47    -1.23
## bf_44    10 98 3.29 1.23      3    3.35 1.48   1   5     4 -0.32    -0.88
##          se
## bf_5   0.10
## bf_10  0.09
## bf_15  0.09
## bf_20  0.10
## bf_25  0.10
## bf_30  0.13
## bf_35R 0.12
## bf_40  0.09
## bf_41R 0.15
## bf_44  0.12

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
bf_5	Big 5 Openness to experience item 5	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.46	0.96	1	3	4	4	5	▁▂▁▆▁▇▁▂
bf_10	Big 5 Openness to experience item 10	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	4.07	0.88	1	4	4	5	5	▁▁▁▂▁▇▁▆
bf_15	Big 5 Openness to experience item 15	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	2.9	0.91	1	2	3	3	5	▁▃▁▇▁▂▁▁
bf_20	Big 5 Openness to experience item 20	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.68	0.99	1	3	4	4	5	▁▂▁▅▁▇▁▅
bf_25	Big 5 Openness to experience item 25	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.54	0.95	1	3	4	4	5	▁▂▁▅▁▇▁▂
bf_30	Big 5 Openness to experience item 30	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.61	1.29	1	3	4	5	5	▂▃▁▅▁▇▁▇
bf_35R	Big 5 Openness to experience item 35	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	2.28	1.14	1	1	2	3	5	▆▇▁▅▁▂▁▁
bf_40	Big 5 Openness to experience item 40	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.88	0.88	1	3	4	4	5	▁▁▁▃▁▇▁▅
bf_41R	Big 5 Openness to experience item 41	numeric	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	2.51	1.47	1	1	2	4	5	▇▅▁▃▁▃▁▃
bf_44	Big 5 Openness to experience item 44	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.27	1.23	1	2	3	4	5	▂▅▁▇▁▇▁▅

Scale: Heteroduperie - Social Desirability

Overview

Reliability: ω_ordinal [95% CI] = 0.86 [0.82;0.9].

Missing: 2.

old_height <- knitr::opts_chunk$get("fig.height")
new_height <- length(scale_info$scale_item_names)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
new_height <- ifelse(is.na(new_height) | is.nan(new_height), 
                     old_height, new_height)
knitr::opts_chunk$set(fig.height = new_height)

if (dplyr::n_distinct(na.omit(unlist(items))) < 12) {
  likert_plot <- likert_from_items(items)
  if (!is.null(likert_plot)) {
    graphics::plot(likert_plot)
  }
}

knitr::opts_chunk$set(fig.height = old_height)

wrap_at <- knitr::opts_chunk$get("fig.width") * 10

dist_plot <- plot_labelled(scale, scale_name, wrap_at)

choices <- attributes(items[[1]])$item$choices
breaks <- as.numeric(names(choices))
if (length(breaks)) {
  suppressMessages( # ignore message about overwriting x axis
  dist_plot <- dist_plot +
        ggplot2::scale_x_continuous("values", 
                                breaks = breaks, 
                                labels = stringr::str_wrap(unlist(choices), ceiling(wrap_at * 0.21))) +
      ggplot2::expand_limits(x = range(breaks)))
  
}

dist_plot

Reliability details

for (i in seq_along(reliabilities)) {
  rel <- reliabilities[[i]]
  cat(knitr::knit_print(rel, indent = paste0(indent, "####")))
}

Reliability Indices

coefs <- x$scaleReliability$output$dat %>% 
  tidyr::gather(index, estimate) %>% 
  dplyr::filter(index != "n.items", index != "n.observations") %>% 
  dplyr::mutate(index = stringr::str_to_title(
    stringr::str_replace_all(index,
      stringr::fixed("."), " ")))

cis <- coefs %>% 
  dplyr::filter(stringr::str_detect(index, " Ci ")) %>% 
  tidyr::separate(index, c("index", "hilo"), sep = " Ci ") %>% 
  tidyr::spread(hilo, estimate)
if (nrow(cis)) {
  cis <- cis %>% dplyr::rename(
    `Lower 95% CI` = .data$Lo, `Upper 95% CI` = .data$Hi
  )
}

coefs_with_cis <- coefs %>% 
  dplyr::filter(!stringr::str_detect(index, " Ci ")) %>% 
    dplyr::left_join(cis, by = "index") %>% 
    dplyr::mutate(index = dplyr::if_else(index == "Glb", "Greatest Lower Bound", .data$index)) %>% 
    dplyr::arrange(!stringr::str_detect(index, "Omega")) %>% 
    dplyr::select(Index = .data$index, Estimate = .data$estimate)


pander::pander(coefs_with_cis)

Index	Estimate
Omega	0.863
Omega Psych Tot	0.8818
Omega Psych H	0.6076
Omega Ordinal	0.8601
Cronbach Alpha	0.8584
Greatest Lower Bound	0.9187
Alpha Ordinal	0.8576

Positive correlations: 152 out of 153 (99%)

Scatter matrix

print(x$scatterMatrix$output$scatterMatrix)

x$scatterMatrix$output$scatterMatrix <- no_md()

Detailed output

print(x)

## 
## Information about this analysis:
## 
##                  Dataframe: res$dat
##                      Items: soc_d_1, soc_d_2R, soc_d_3, soc_d_4R, soc_d_5R, soc_d_6, soc_d_7R, soc_d_8, soc_d_9, soc_d_10R, soc_d_11R, soc_d_12, soc_d_13R, soc_d_14R, soc_d_15, soc_d_16, soc_d_17R, soc_d_18
##               Observations: 97
##      Positive correlations: 152 out of 153 (99%)
## 
## Estimates assuming interval level:
## 
##              Omega (total): 0.86
##       Omega (hierarchical): 0.61
##    Revelle's omega (total): 0.88
## Greatest Lower Bound (GLB): 0.92
##              Coefficient H: 0.88
##           Cronbach's alpha: 0.86
## Confidence intervals:
##              Omega (total): [0.82, 0.9]
##           Cronbach's alpha: [0.82, 0.9]
## 
## Estimates assuming ordinal level:
## 
##      Ordinal Omega (total): 0.86
##  Ordinal Omega (hierarch.): 0.85
##   Ordinal Cronbach's alpha: 0.86
## Confidence intervals:
##      Ordinal Omega (total): [0.82, 0.9]
##   Ordinal Cronbach's alpha: [0.82, 0.9]
## 
## Note: the normal point estimate and confidence interval for omega are based on the procedure suggested by Dunn, Baguley & Brunsden (2013) using the MBESS function ci.reliability, whereas the psych package point estimate was suggested in Revelle & Zinbarg (2008). See the help ('?scaleStructure') for more information.
## 
## Eigen values: 5.489, 1.597, 1.432, 1.231, 1.111, 0.933, 0.907, 0.826, 0.723, 0.697, 0.551, 0.529, 0.486, 0.41, 0.326, 0.306, 0.278, 0.169
## Loadings:
##           TC1    TC2    TC3    TC4    TC5   
## soc_d_1           0.106                0.883
## soc_d_2R   0.814                0.174 -0.116
## soc_d_3           0.193  0.767        -0.387
## soc_d_4R   0.167  0.260  0.365 -0.336  0.250
## soc_d_5R   0.609 -0.320  0.243         0.168
## soc_d_6           0.775                0.210
## soc_d_7R   0.691         0.140         0.113
## soc_d_8           0.690         0.130       
## soc_d_9           0.629 -0.147  0.171       
## soc_d_10R        -0.119  0.786  0.249  0.139
## soc_d_11R  0.311  0.109  0.623 -0.121       
## soc_d_12          0.328  0.120  0.444  0.241
## soc_d_13R  0.722        -0.171        -0.106
## soc_d_14R  0.264  0.570  0.198 -0.274  0.151
## soc_d_15   0.291         0.189  0.293  0.258
## soc_d_16   0.442  0.371 -0.128  0.367 -0.103
## soc_d_17R  0.667  0.113        -0.108       
## soc_d_18                 0.177  0.782  0.109
## 
##                  TC1   TC2   TC3   TC4   TC5
## SS loadings    2.979 2.316 2.016 1.405 1.311
## Proportion Var 0.166 0.129 0.112 0.078 0.073
## Cumulative Var 0.166 0.294 0.406 0.484 0.557
## 
##           vars  n mean   sd median trimmed  mad min max range  skew
## soc_d_1      1 97 4.14 0.85      4    4.24 1.48   1   5     4 -0.97
## soc_d_2R     2 97 2.87 1.31      2    2.84 1.48   1   5     4  0.49
## soc_d_3      3 97 3.23 1.03      3    3.25 1.48   1   5     4 -0.34
## soc_d_4R     4 97 1.97 1.15      2    1.78 1.48   1   5     4  1.16
## soc_d_5R     5 97 3.16 1.20      3    3.18 1.48   1   5     4 -0.03
## soc_d_6      6 97 3.10 1.33      3    3.13 1.48   1   5     4 -0.03
## soc_d_7R     7 97 3.09 1.29      3    3.11 1.48   1   5     4  0.06
## soc_d_8      8 97 3.24 1.40      3    3.29 1.48   1   5     4 -0.15
## soc_d_9      9 97 3.57 1.26      4    3.67 1.48   1   5     4 -0.46
## soc_d_10R   10 97 3.25 1.21      3    3.25 1.48   1   5     4 -0.06
## soc_d_11R   11 97 3.14 1.25      3    3.15 1.48   1   5     4  0.05
## soc_d_12    12 97 3.67 1.12      4    3.76 1.48   1   5     4 -0.63
## soc_d_13R   13 97 3.18 1.36      3    3.22 1.48   1   5     4 -0.02
## soc_d_14R   14 97 3.13 1.35      3    3.16 1.48   1   5     4 -0.02
## soc_d_15    15 97 3.62 1.03      4    3.67 1.48   1   5     4 -0.52
## soc_d_16    16 97 3.15 1.17      3    3.19 1.48   1   5     4 -0.18
## soc_d_17R   17 97 3.11 1.22      3    3.14 1.48   1   5     4 -0.08
## soc_d_18    18 97 3.49 1.37      4    3.61 1.48   1   5     4 -0.42
##           kurtosis   se
## soc_d_1       0.98 0.09
## soc_d_2R     -1.07 0.13
## soc_d_3      -0.44 0.10
## soc_d_4R      0.49 0.12
## soc_d_5R     -1.06 0.12
## soc_d_6      -1.22 0.14
## soc_d_7R     -1.30 0.13
## soc_d_8      -1.38 0.14
## soc_d_9      -0.88 0.13
## soc_d_10R    -1.22 0.12
## soc_d_11R    -1.24 0.13
## soc_d_12     -0.45 0.11
## soc_d_13R    -1.25 0.14
## soc_d_14R    -1.35 0.14
## soc_d_15     -0.51 0.10
## soc_d_16     -0.87 0.12
## soc_d_17R    -1.03 0.12
## soc_d_18     -1.08 0.14

Summary statistics

for (i in seq_along(names(items))) {
  attributes(items[[i]]) = recursive_escape(attributes(items[[i]]))
}
escaped_table(codebook_table(items))

name	label	data_type	value_labels	missing	complete	n	mean	sd	p0	p25	p50	p75	p100	hist
soc_d_1	Social desirability item 1	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	4.14	0.85	1	4	4	5	5	▁▁▁▂▁▇▁▇
soc_d_2R	Social desirability item 2	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	98	99	2.88	1.31	1	2	2	4	5	▂▇▁▃▁▂▁▃
soc_d_3	Social desirability item 3	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.22	1.02	1	3	3	4	5	▂▃▁▇▁▇▁▂
soc_d_4R	Social desirability item 4	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	98	99	1.97	1.14	1	1	2	2	5	▇▆▁▂▁▁▁▁
soc_d_5R	Social desirability item 5	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	98	99	3.15	1.2	1	2	3	4	5	▂▇▁▇▁▇▁▅
soc_d_6	Social desirability item 6	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.1	1.33	1	2	3	4	5	▅▇▁▇▁▇▁▆
soc_d_7R	Social desirability item 7	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	98	99	3.08	1.29	1	2	3	4	5	▂▇▁▃▁▆▁▅
soc_d_8	Social desirability item 8	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.24	1.39	1	2	3.5	4	5	▃▇▁▃▁▇▁▇
soc_d_9	Social desirability item 9	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.55	1.26	1	3	4	5	5	▂▅▁▆▁▇▁▇
soc_d_10R	Social desirability item 10	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	98	99	3.23	1.21	1	2	3	4	5	▁▇▁▅▁▇▁▅
soc_d_11R	Social desirability item 11	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	1	98	99	3.13	1.25	1	2	3	4	5	▂▇▁▅▁▆▁▅
soc_d_12	Social desirability item 12	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.65	1.12	1	3	4	4	5	▁▃▁▃▁▇▁▅
soc_d_13R	Social desirability item 13	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	3.16	1.35	1	2	3	4	5	▃▇▁▇▁▅▁▇
soc_d_14R	Social desirability item 14	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	3.12	1.34	1	2	3	4	5	▃▇▁▃▁▆▁▅
soc_d_15	Social desirability item 15	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.64	1.02	1	3	4	4	5	▁▃▁▃▁▇▁▃
soc_d_16	Social desirability item 16	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	0	99	99	3.16	1.18	1	2	3	4	5	▂▆▁▇▁▇▁▃
soc_d_17R	Social desirability item 17	numeric	5. Disapprove strongly, 4. Disapprove slightly, 3. Neither approve nore disapprove, 2. Approve slightly, 1. Approve strongly	0	99	99	3.09	1.22	1	2	3	4	5	▃▇▁▇▁▇▁▅
soc_d_18	Social desirability item 18	integer	1. Disapprove strongly, 2. Disapprove slightly, 3. Neither approve nore disapprove, 4. Approve slightly, 5. Approve strongly	1	98	99	3.48	1.37	1	2.25	4	5	5	▃▃▁▆▁▅▁▇

missingness_report

Missingness report

if (length(md_pattern)) {
  if (knitr::is_html_output()) {
    rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
  } else {
    knitr::kable(md_pattern)
  }
}

items

Codebook table

export_table(metadata_table)

jsonld

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "Non-Shared Condition: French-Belgian student data on Big 5, Social Desirability as measured by Heteroduperie and Anchoring Paradigm, public data set",
  "description": "45 items taking from the french translation of the Big 5 Personality questionnaire (Plaisant et al. 2010), 18 items from the subscale 'Hétéroduperie' of the french social desirability scale (Tournois et al., 2010) and 3 Anchoring paradigm items as used in the ManyLabs replication project (Klein et al., 2014). Also includes 7 careless response items based on Meade and Craig (2012). This part of the dataset cannot be publicly shared, as the study consent stated that we would not provide public access to the data. If you are interested in re-analyzing the entire dataset, please contact the authors\n\n\n## Table of variables\nThis table contains variable names, labels, their central tendencies and other attributes.\n\n|name                                |label                                                                                                                       |data_type |value_labels                                                                                                                       |scale_item_names                                                                                                                                                                   |missing |complete |n  |empty |n_unique |min |max |mean     |sd        |p0    |p25  |p50   |p75   |p100  |hist     |\n|:-----------------------------------|:---------------------------------------------------------------------------------------------------------------------------|:---------|:----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------|:--------|:--|:-----|:--------|:---|:---|:--------|:---------|:-----|:----|:-----|:-----|:-----|:--------|\n|V1                                  |NA                                                                                                                          |integer   |NA                                                                                                                                 |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |96.11    |54.16     |1     |54.5 |93    |142   |192   |▅▆▆▇▆▆▆▆ |\n|id                                  |NA                                                                                                                          |character |NA                                                                                                                                 |NA                                                                                                                                                                                 |0       |99       |99 |0     |99       |4   |5   |NA       |NA        |NA    |NA   |NA    |NA    |NA    |NA       |\n|participant                         |NA                                                                                                                          |character |NA                                                                                                                                 |NA                                                                                                                                                                                 |0       |99       |99 |0     |99       |4   |5   |NA       |NA        |NA    |NA   |NA    |NA    |NA    |NA       |\n|gender                              |Reported gender information                                                                                                 |integer   |1. Female, - 2. Male                                                                                                                 |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |1.38     |0.49      |1     |1    |1     |2     |2     |▇▁▁▁▁▁▁▅ |\n|acad_status                         |Academic status                                                                                                             |integer   |0. other, - 1. bachelor, - 2. master, - 3. PhD, - 4. other                                                                                 |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |1.17     |0.54      |0     |1    |1     |1     |3     |▁▁▇▁▁▂▁▁ |\n|age                                 |Age Group                                                                                                                   |integer   |1. 18-29 yo, - 2. 21-25 yo, - 3. 26-30 yo, - 4. 31-35 yo, - 5. 36-40 yo, - 6. 41-50 yo, - 7. 51-60 yo, - 8. 61-70 yo, - 9. 71-80 yo, - 10. 81-110 yo |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |1.66     |0.93      |1     |1    |1     |2     |7     |▇▅▂▁▁▁▁▁ |\n|refused                             |Refused participation after consent                                                                                         |integer   |0. No, - 1. Yes                                                                                                                      |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |0        |0         |0     |0    |0     |0     |0     |▁▁▁▇▁▁▁▁ |\n|reason                              |Reason for refusal                                                                                                          |integer   |0. Not refused, - 1. Data sharing, - 2. Not enough time, - 3. Other                                                                      |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |0        |0         |0     |0    |0     |0     |0     |▁▁▁▇▁▁▁▁ |\n|consent                             |Data sharing policy in consent                                                                                              |character |A. Data will be shared, - B. Data will not be shared                                                                                 |NA                                                                                                                                                                                 |0       |99       |99 |0     |1        |1   |1   |NA       |NA        |NA    |NA   |NA    |NA    |NA    |NA       |\n|bf_1                                |Big 5 Extraversion item 1                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.55     |1.17      |1     |3    |4     |4.5   |5     |▂▅▁▇▁▇▁▇ |\n|bf_2R                               |Big 5 Agreeableness item 2                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.6      |1.12      |1     |3    |4     |4     |5     |▁▃▁▅▁▇▁▅ |\n|bf_3                                |Big 5 Conscientiousness item 3                                                                                              |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.52     |1.24      |1     |3    |4     |5     |5     |▂▃▁▇▁▇▁▇ |\n|bf_4                                |Big 5 Neuroticism item 4                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |2.02     |1.23      |1     |1    |2     |3     |5     |▇▃▁▂▁▂▁▁ |\n|bf_5                                |Big 5 Openness to experience item 5                                                                                         |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.46     |0.96      |1     |3    |4     |4     |5     |▁▂▁▆▁▇▁▂ |\n|bf_6R                               |Big 5 Extraversion item 6                                                                                                   |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.03     |1.4       |1     |2    |3     |4     |5     |▆▅▁▅▁▇▁▅ |\n|bf_7                                |Big 5 Agreeableness item 7                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |4.17     |0.9       |2     |4    |4     |5     |5     |▁▁▃▁▁▆▁▇ |\n|bf_8R                               |Big 5 Conscientiousness item 8                                                                                              |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |2.85     |1.24      |1     |2    |3     |4     |5     |▅▇▁▆▁▇▁▂ |\n|bf_9R                               |Big 5 Neuroticism item 9                                                                                                    |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.13     |1.28      |1     |2    |3     |4     |5     |▃▇▁▇▁▆▁▆ |\n|bf_10                               |Big 5 Openness to experience item 10                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |4.07     |0.88      |1     |4    |4     |5     |5     |▁▁▁▂▁▇▁▆ |\n|bf_11                               |Big 5 Extraversion item 11                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.7      |1.05      |1     |3    |4     |4     |5     |▁▂▁▅▁▇▁▅ |\n|bf_12R                              |Big 5 Agreeableness item 12                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.96     |1.2       |1     |3    |4     |5     |5     |▁▂▁▂▁▅▁▇ |\n|bf_13                               |Big 5 Conscientiousness item 13                                                                                             |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.97     |0.98      |1     |3    |4     |5     |5     |▁▁▁▆▁▇▁▇ |\n|bf_14                               |Big 5 Neuroticism item 14                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.14     |1.32      |1     |2    |3     |4     |5     |▃▅▁▃▁▇▁▃ |\n|bf_15                               |Big 5 Openness to experience item 15                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |2.9      |0.91      |1     |2    |3     |3     |5     |▁▃▁▇▁▂▁▁ |\n|bf_16                               |Big 5 Extraversion item 16                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.68     |1.16      |1     |3    |4     |5     |5     |▁▃▁▃▁▇▁▆ |\n|bf_17                               |Big 5 Agreeableness item 17                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.49     |1.06      |1     |3    |4     |4     |5     |▁▃▁▃▁▇▁▃ |\n|bf_18R                              |Big 5 Conscientiousness item 18                                                                                             |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |2.8      |1.34      |1     |2    |3     |4     |5     |▇▅▁▇▁▇▁▃ |\n|bf_19                               |Big 5 Neuroticism item 19                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.19     |1.3       |1     |2    |3     |4     |5     |▃▅▁▆▁▇▁▅ |\n|bf_20                               |Big 5 Openness to experience item 20                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.68     |0.99      |1     |3    |4     |4     |5     |▁▂▁▅▁▇▁▅ |\n|bf_21R                              |Big 5 Extraversion item 21                                                                                                  |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |97       |99 |NA    |NA       |NA  |NA  |2.8      |1.4       |1     |1    |3     |4     |5     |▇▃▁▆▁▇▁▃ |\n|bf_22                               |Big 5 Agreeableness item 22                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.41     |1.21      |1     |3    |4     |4     |5     |▂▃▁▃▁▇▁▃ |\n|bf_23R                              |Big 5 Conscientiousness item 23                                                                                             |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.07     |1.26      |1     |2    |3     |4     |5     |▃▆▁▇▁▆▁▅ |\n|bf_24R                              |Big 5 Neuroticism item 24                                                                                                   |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.13     |1.3       |1     |2    |3     |4     |5     |▃▆▁▇▁▆▁▆ |\n|bf_25                               |Big 5 Openness to experience item 25                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.54     |0.95      |1     |3    |4     |4     |5     |▁▂▁▅▁▇▁▂ |\n|bf_26                               |Big 5 Extraversion item 26                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.42     |1.16      |1     |3    |4     |4     |5     |▂▅▁▆▁▇▁▅ |\n|bf_27R                              |Big 5 Agreeableness item 27                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |4        |1.12      |1     |3    |4     |5     |5     |▁▂▁▃▁▅▁▇ |\n|bf_28                               |Big 5 Conscientiousness item 28                                                                                             |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.83     |1.1       |1     |3    |4     |5     |5     |▁▂▁▃▁▇▁▇ |\n|bf_29                               |Big 5 Neuroticism item 29                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |2.94     |1.4       |1     |2    |3     |4     |5     |▇▇▁▆▁▇▁▅ |\n|bf_30                               |Big 5 Openness to experience item 30                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.61     |1.29      |1     |3    |4     |5     |5     |▂▃▁▅▁▇▁▇ |\n|bf_31R                              |Big 5 Extraversion item 31                                                                                                  |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |2.99     |1.34      |1     |2    |3     |4     |5     |▆▅▁▆▁▇▁▃ |\n|bf_32                               |Big 5 Agreeableness item 32                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.9      |0.97      |1     |3.25 |4     |5     |5     |▁▂▁▂▁▇▁▅ |\n|bf_33                               |Big 5 Conscientiousness item 33                                                                                             |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.77     |0.93      |1     |3    |4     |4     |5     |▁▁▁▃▁▇▁▃ |\n|bf_34R                              |Big 5 Neuroticism item 34                                                                                                   |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |2.87     |1.29      |1     |2    |3     |4     |5     |▆▇▁▇▁▆▁▅ |\n|bf_35R                              |Big 5 Openness to experience item 35                                                                                        |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |2.28     |1.14      |1     |1    |2     |3     |5     |▆▇▁▅▁▂▁▁ |\n|bf_36                               |Big 5 Extraversion item 36                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.28     |1.29      |1     |2    |3     |4     |5     |▃▅▁▆▁▇▁▆ |\n|bf_37R                              |Big 5 Agreeableness item 37                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |4.03     |1.08      |1     |3    |4     |5     |5     |▁▂▁▂▁▅▁▇ |\n|bf_38R                              |Big 5 Conscientiousness item 38                                                                                             |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.6      |1.08      |1     |3    |4     |4     |5     |▁▂▁▅▁▇▁▅ |\n|bf_39                               |Big 5 Neuroticism item 39                                                                                                   |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |2.85     |1.3       |1     |2    |3     |4     |5     |▅▇▁▆▁▆▁▃ |\n|bf_40                               |Big 5 Openness to experience item 40                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.88     |0.88      |1     |3    |4     |4     |5     |▁▁▁▃▁▇▁▅ |\n|bf_41R                              |Big 5 Openness to experience item 41                                                                                        |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |2.51     |1.47      |1     |1    |2     |4     |5     |▇▅▁▃▁▃▁▃ |\n|bf_42                               |Big 5 Agreeableness item 42                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.69     |1.17      |1     |3    |4     |5     |5     |▂▂▁▅▁▇▁▆ |\n|bf_43R                              |Big 5 Conscientiousness item 43                                                                                             |numeric   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.41     |1.37      |1     |2.5  |4     |4.5   |5     |▃▂▁▅▁▇▁▆ |\n|bf_44                               |Big 5 Openness to experience item 44                                                                                        |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.27     |1.23      |1     |2    |3     |4     |5     |▂▅▁▇▁▇▁▅ |\n|bf_45R                              |Big 5 Agreeableness item 45                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |4.36     |0.92      |1     |4    |5     |5     |5     |▁▁▁▁▁▃▁▇ |\n|cr_1                                |Careless response item 1                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |1.25     |0.58      |1     |1    |1     |1     |4     |▇▁▂▁▁▁▁▁ |\n|cr_2                                |Careless response item 2                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |2       |97       |99 |NA    |NA       |NA  |NA  |1.68     |1.11      |1     |1    |1     |2     |5     |▇▁▁▂▁▁▁▁ |\n|cr_3                                |Careless response item 3                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |1.74     |1.13      |1     |1    |1     |2.75  |5     |▇▂▁▂▁▁▁▁ |\n|cr_4                                |Careless response item 4                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |1.22     |0.65      |1     |1    |1     |1     |5     |▇▁▁▁▁▁▁▁ |\n|cr_5                                |Careless response item 5                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |1.17     |0.66      |1     |1    |1     |1     |5     |▇▁▁▁▁▁▁▁ |\n|cr_6                                |Careless response item 6                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |1.72     |1.42      |1     |1    |1     |1     |5     |▇▁▁▁▁▁▁▁ |\n|cr_7                                |Careless response item 7                                                                                                    |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |1.64     |1.34      |1     |1    |1     |1     |5     |▇▁▁▁▁▁▁▁ |\n|soc_d_1                             |Social desirability item 1                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |4.14     |0.85      |1     |4    |4     |5     |5     |▁▁▁▂▁▇▁▇ |\n|soc_d_2R                            |Social desirability item 2                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |2.88     |1.31      |1     |2    |2     |4     |5     |▂▇▁▃▁▂▁▃ |\n|soc_d_3                             |Social desirability item 3                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.22     |1.02      |1     |3    |3     |4     |5     |▂▃▁▇▁▇▁▂ |\n|soc_d_4R                            |Social desirability item 4                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |1.97     |1.14      |1     |1    |2     |2     |5     |▇▆▁▂▁▁▁▁ |\n|soc_d_5R                            |Social desirability item 5                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.15     |1.2       |1     |2    |3     |4     |5     |▂▇▁▇▁▇▁▅ |\n|soc_d_6                             |Social desirability item 6                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.1      |1.33      |1     |2    |3     |4     |5     |▅▇▁▇▁▇▁▆ |\n|soc_d_7R                            |Social desirability item 7                                                                                                  |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.08     |1.29      |1     |2    |3     |4     |5     |▂▇▁▃▁▆▁▅ |\n|soc_d_8                             |Social desirability item 8                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.24     |1.39      |1     |2    |3.5   |4     |5     |▃▇▁▃▁▇▁▇ |\n|soc_d_9                             |Social desirability item 9                                                                                                  |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.55     |1.26      |1     |3    |4     |5     |5     |▂▅▁▆▁▇▁▇ |\n|soc_d_10R                           |Social desirability item 10                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.23     |1.21      |1     |2    |3     |4     |5     |▁▇▁▅▁▇▁▅ |\n|soc_d_11R                           |Social desirability item 11                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.13     |1.25      |1     |2    |3     |4     |5     |▂▇▁▅▁▆▁▅ |\n|soc_d_12                            |Social desirability item 12                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.65     |1.12      |1     |3    |4     |4     |5     |▁▃▁▃▁▇▁▅ |\n|soc_d_13R                           |Social desirability item 13                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.16     |1.35      |1     |2    |3     |4     |5     |▃▇▁▇▁▅▁▇ |\n|soc_d_14R                           |Social desirability item 14                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.12     |1.34      |1     |2    |3     |4     |5     |▃▇▁▃▁▆▁▅ |\n|soc_d_15                            |Social desirability item 15                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.64     |1.02      |1     |3    |4     |4     |5     |▁▃▁▃▁▇▁▃ |\n|soc_d_16                            |Social desirability item 16                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.16     |1.18      |1     |2    |3     |4     |5     |▂▆▁▇▁▇▁▃ |\n|soc_d_17R                           |Social desirability item 17                                                                                                 |numeric   |5. Disapprove strongly, - 4. Disapprove slightly, - 3. Neither approve nore disapprove, - 2. Approve slightly, - 1. Approve strongly       |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |3.09     |1.22      |1     |2    |3     |4     |5     |▃▇▁▇▁▇▁▅ |\n|soc_d_18                            |Social desirability item 18                                                                                                 |integer   |1. Disapprove strongly, - 2. Disapprove slightly, - 3. Neither approve nore disapprove, - 4. Approve slightly, - 5. Approve strongly       |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |3.48     |1.37      |1     |2.25 |4     |5     |5     |▃▃▁▆▁▅▁▇ |\n|cond_anc                            |Anchoring condition                                                                                                         |integer   |0. Low anchoring value, - 1. High anchoring value                                                                                    |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |0.51     |0.5       |0     |0    |1     |1     |1     |▇▁▁▁▁▁▁▇ |\n|anc_everest                         |Anchoring: How high is Mount Everest                                                                                        |integer   |NA                                                                                                                                 |NA                                                                                                                                                                                 |5       |94       |99 |NA    |NA       |NA  |NA  |7691.74  |5263.54   |600   |2850 |8000  |10000 |30000 |▇▃▇▅▁▁▁▁ |\n|anc_chicago                         |Anchoring: Population of Chicagor                                                                                           |numeric   |NA                                                                                                                                 |NA                                                                                                                                                                                 |6       |93       |99 |NA    |NA       |NA  |NA  |34.12    |258.87    |0.004 |1.5  |3     |6     |2500  |▇▁▁▁▁▁▁▁ |\n|anc_bebe                            |Anchoring: Babies born in US                                                                                                |integer   |NA                                                                                                                                 |NA                                                                                                                                                                                 |8       |91       |99 |NA    |NA       |NA  |NA  |74118.87 |167458.67 |80    |1250 |15000 |50500 |1e+06 |▇▁▁▁▁▁▁▁ |\n|mc_1                                |Manipulation check, question 1: 'Do you remember the consent you signed in the beginning?'                                  |integer   |1. Yes, - 0. No                                                                                                                      |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |0.97     |0.17      |0     |1    |1     |1     |1     |▁▁▁▁▁▁▁▇ |\n|mc_2                                |Manipulation check, question 2: 'Do you remember if the consent contained the topic of sharing anonymous data with others?' |integer   |1. Yes, - 0. No                                                                                                                      |NA                                                                                                                                                                                 |0       |99       |99 |NA    |NA       |NA  |NA  |0.66     |0.48      |0     |0    |1     |1     |1     |▅▁▁▁▁▁▁▇ |\n|mc_3                                |Manipulation check, question: 'Will your anonymous data be shared with others?'                                             |integer   |1. Yes, - 0. No                                                                                                                      |NA                                                                                                                                                                                 |1       |98       |99 |NA    |NA       |NA  |NA  |0.33     |0.47      |0     |0    |0     |1     |1     |▇▁▁▁▁▁▁▃ |\n|remarks                             |Observer remarks                                                                                                            |character |NA                                                                                                                                 |NA                                                                                                                                                                                 |0       |99       |99 |86    |14       |0   |69  |NA       |NA        |NA    |NA   |NA    |NA    |NA    |NA       |\n|Extraversion                        |8 bf items aggregated by rowMeans                                                                                           |numeric   |NA                                                                                                                                 |bf_1, bf_6R, bf_11, bf_16, bf_21R, bf_26, bf_31R, bf_36                                                                                                                            |3       |96       |99 |NA    |NA       |NA  |NA  |3.33     |0.97      |1.5   |2.5  |3.31  |4.16  |5     |▃▇▅▇▃▇▇▅ |\n|Agreeableness                       |10 bf items aggregated by rowMeans                                                                                          |numeric   |NA                                                                                                                                 |bf_2R, bf_7, bf_12R, bf_17, bf_22, bf_27R, bf_32, bf_37R, bf_42, bf_45R                                                                                                            |2       |97       |99 |NA    |NA       |NA  |NA  |3.88     |0.59      |2.2   |3.4  |3.9   |4.3   |5     |▁▂▂▇▅▇▆▂ |\n|Conscientiousness                   |9 bf items aggregated by rowMeans                                                                                           |numeric   |NA                                                                                                                                 |bf_3, bf_8R, bf_13, bf_18R, bf_23R, bf_28, bf_33, bf_38R, bf_43R                                                                                                                   |2       |97       |99 |NA    |NA       |NA  |NA  |3.27     |0.63      |1.78  |2.89 |3.22  |3.67  |4.56  |▂▂▂▇▆▃▃▃ |\n|Neuroticism                         |8 bf items aggregated by rowMeans                                                                                           |numeric   |NA                                                                                                                                 |bf_4, bf_9R, bf_14, bf_19, bf_24R, bf_29, bf_34R, bf_39                                                                                                                            |1       |98       |99 |NA    |NA       |NA  |NA  |2.92     |0.88      |1.25  |2.12 |2.94  |3.5   |4.75  |▂▇▅▇▆▃▃▃ |\n|Openness to experience              |10 bf items aggregated by rowMeans                                                                                          |numeric   |NA                                                                                                                                 |bf_5, bf_10, bf_15, bf_20, bf_25, bf_30, bf_35R, bf_40, bf_41R, bf_44                                                                                                              |1       |98       |99 |NA    |NA       |NA  |NA  |3.56     |0.54      |2.1   |3.2  |3.55  |3.98  |4.6   |▁▂▅▇▇▇▇▃ |\n|Heteroduperie - Social Desirability |18 soc_d items aggregated by rowMeans                                                                                       |numeric   |NA                                                                                                                                 |soc_d_1, soc_d_2R, soc_d_3, soc_d_4R, soc_d_5R, soc_d_6, soc_d_7R, soc_d_8, soc_d_9, soc_d_10R, soc_d_11R, soc_d_12, soc_d_13R, soc_d_14R, soc_d_15, soc_d_16, soc_d_17R, soc_d_18 |2       |97       |99 |NA    |NA       |NA  |NA  |3.23     |0.66      |1.78  |2.78 |3.22  |3.61  |4.89  |▁▆▆▇▇▃▂▂ |\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.1).",
  "identifier": "https://dx.doi.org/10.17605/OSF.IO/X25D3",
  "creator": "Julia C. Eberlen, Emmanuel Nicaise, Sarah Leveaux, Youri L. Mora, Olivier Klein",
  "citation": "Eberlen, J. C., Nicaise, E., Leveaux, S., Mora, Y., & Klein, O. (2019, August 5). Impact of Data sharing: Data collected offline. https://doi.org/10.17605/OSF.IO/X25D3",
  "datePublished": "2019-08-05",
  "temporalCoverage": "2018-12-03 to 2018-12-17",
  "spatialCoverage": "Campus Solbosch, Universite libre de Bruxelles, Brussels, Belgium",
  "keywords": ["V1", "id", "participant", "gender", "acad_status", "age", "refused", "reason", "consent", "bf_1", "bf_2R", "bf_3", "bf_4", "bf_5", "bf_6R", "bf_7", "bf_8R", "bf_9R", "bf_10", "bf_11", "bf_12R", "bf_13", "bf_14", "bf_15", "bf_16", "bf_17", "bf_18R", "bf_19", "bf_20", "bf_21R", "bf_22", "bf_23R", "bf_24R", "bf_25", "bf_26", "bf_27R", "bf_28", "bf_29", "bf_30", "bf_31R", "bf_32", "bf_33", "bf_34R", "bf_35R", "bf_36", "bf_37R", "bf_38R", "bf_39", "bf_40", "bf_41R", "bf_42", "bf_43R", "bf_44", "bf_45R", "cr_1", "cr_2", "cr_3", "cr_4", "cr_5", "cr_6", "cr_7", "soc_d_1", "soc_d_2R", "soc_d_3", "soc_d_4R", "soc_d_5R", "soc_d_6", "soc_d_7R", "soc_d_8", "soc_d_9", "soc_d_10R", "soc_d_11R", "soc_d_12", "soc_d_13R", "soc_d_14R", "soc_d_15", "soc_d_16", "soc_d_17R", "soc_d_18", "cond_anc", "anc_everest", "anc_chicago", "anc_bebe", "mc_1", "mc_2", "mc_3", "remarks", "Extraversion", "Agreeableness", "Conscientiousness", "Neuroticism", "Openness to experience", "Heteroduperie - Social Desirability"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "V1",
      "@type": "propertyValue"
    },
    {
      "name": "id",
      "@type": "propertyValue"
    },
    {
      "name": "participant",
      "@type": "propertyValue"
    },
    {
      "name": "gender",
      "description": "Reported gender information",
      "value": "1. Female,\n2. Male",
      "maxValue": 2,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "acad_status",
      "description": "Academic status",
      "value": "0. other,\n1. bachelor,\n2. master,\n3. PhD,\n4. other",
      "maxValue": 4,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "age",
      "description": "Age Group",
      "value": "1. 18-29 yo,\n2. 21-25 yo,\n3. 26-30 yo,\n4. 31-35 yo,\n5. 36-40 yo,\n6. 41-50 yo,\n7. 51-60 yo,\n8. 61-70 yo,\n9. 71-80 yo,\n10. 81-110 yo",
      "maxValue": 10,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "refused",
      "description": "Refused participation after consent",
      "value": "0. No,\n1. Yes",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "reason",
      "description": "Reason for refusal",
      "value": "0. Not refused,\n1. Data sharing,\n2. Not enough time,\n3. Other",
      "maxValue": 3,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "consent",
      "description": "Data sharing policy in consent",
      "value": "A. Data will be shared,\nB. Data will not be shared",
      "maxValue": "B",
      "minValue": "A",
      "@type": "propertyValue"
    },
    {
      "name": "bf_1",
      "description": "Big 5 Extraversion item 1",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_2R",
      "description": "Big 5 Agreeableness item 2",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_3",
      "description": "Big 5 Conscientiousness item 3",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_4",
      "description": "Big 5 Neuroticism item 4",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_5",
      "description": "Big 5 Openness to experience item 5",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_6R",
      "description": "Big 5 Extraversion item 6",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_7",
      "description": "Big 5 Agreeableness item 7",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_8R",
      "description": "Big 5 Conscientiousness item 8",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_9R",
      "description": "Big 5 Neuroticism item 9",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_10",
      "description": "Big 5 Openness to experience item 10",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_11",
      "description": "Big 5 Extraversion item 11",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_12R",
      "description": "Big 5 Agreeableness item 12",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_13",
      "description": "Big 5 Conscientiousness item 13",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_14",
      "description": "Big 5 Neuroticism item 14",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_15",
      "description": "Big 5 Openness to experience item 15",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_16",
      "description": "Big 5 Extraversion item 16",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_17",
      "description": "Big 5 Agreeableness item 17",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_18R",
      "description": "Big 5 Conscientiousness item 18",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_19",
      "description": "Big 5 Neuroticism item 19",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_20",
      "description": "Big 5 Openness to experience item 20",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_21R",
      "description": "Big 5 Extraversion item 21",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_22",
      "description": "Big 5 Agreeableness item 22",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_23R",
      "description": "Big 5 Conscientiousness item 23",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_24R",
      "description": "Big 5 Neuroticism item 24",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_25",
      "description": "Big 5 Openness to experience item 25",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_26",
      "description": "Big 5 Extraversion item 26",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_27R",
      "description": "Big 5 Agreeableness item 27",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_28",
      "description": "Big 5 Conscientiousness item 28",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_29",
      "description": "Big 5 Neuroticism item 29",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_30",
      "description": "Big 5 Openness to experience item 30",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_31R",
      "description": "Big 5 Extraversion item 31",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_32",
      "description": "Big 5 Agreeableness item 32",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_33",
      "description": "Big 5 Conscientiousness item 33",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_34R",
      "description": "Big 5 Neuroticism item 34",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_35R",
      "description": "Big 5 Openness to experience item 35",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_36",
      "description": "Big 5 Extraversion item 36",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_37R",
      "description": "Big 5 Agreeableness item 37",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_38R",
      "description": "Big 5 Conscientiousness item 38",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_39",
      "description": "Big 5 Neuroticism item 39",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_40",
      "description": "Big 5 Openness to experience item 40",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_41R",
      "description": "Big 5 Openness to experience item 41",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_42",
      "description": "Big 5 Agreeableness item 42",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_43R",
      "description": "Big 5 Conscientiousness item 43",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_44",
      "description": "Big 5 Openness to experience item 44",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "bf_45R",
      "description": "Big 5 Agreeableness item 45",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_1",
      "description": "Careless response item 1",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_2",
      "description": "Careless response item 2",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_3",
      "description": "Careless response item 3",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_4",
      "description": "Careless response item 4",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_5",
      "description": "Careless response item 5",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_6",
      "description": "Careless response item 6",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cr_7",
      "description": "Careless response item 7",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_1",
      "description": "Social desirability item 1",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_2R",
      "description": "Social desirability item 2",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_3",
      "description": "Social desirability item 3",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_4R",
      "description": "Social desirability item 4",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_5R",
      "description": "Social desirability item 5",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_6",
      "description": "Social desirability item 6",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_7R",
      "description": "Social desirability item 7",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_8",
      "description": "Social desirability item 8",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_9",
      "description": "Social desirability item 9",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_10R",
      "description": "Social desirability item 10",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_11R",
      "description": "Social desirability item 11",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_12",
      "description": "Social desirability item 12",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_13R",
      "description": "Social desirability item 13",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_14R",
      "description": "Social desirability item 14",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_15",
      "description": "Social desirability item 15",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_16",
      "description": "Social desirability item 16",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_17R",
      "description": "Social desirability item 17",
      "value": "5. Disapprove strongly,\n4. Disapprove slightly,\n3. Neither approve nore disapprove,\n2. Approve slightly,\n1. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "soc_d_18",
      "description": "Social desirability item 18",
      "value": "1. Disapprove strongly,\n2. Disapprove slightly,\n3. Neither approve nore disapprove,\n4. Approve slightly,\n5. Approve strongly",
      "maxValue": 5,
      "minValue": 1,
      "@type": "propertyValue"
    },
    {
      "name": "cond_anc",
      "description": "Anchoring condition",
      "value": "0. Low anchoring value,\n1. High anchoring value",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "anc_everest",
      "description": "Anchoring: How high is Mount Everest",
      "@type": "propertyValue"
    },
    {
      "name": "anc_chicago",
      "description": "Anchoring: Population of Chicagor",
      "@type": "propertyValue"
    },
    {
      "name": "anc_bebe",
      "description": "Anchoring: Babies born in US",
      "@type": "propertyValue"
    },
    {
      "name": "mc_1",
      "description": "Manipulation check, question 1: 'Do you remember the consent you signed in the beginning?'",
      "value": "1. Yes,\n0. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "mc_2",
      "description": "Manipulation check, question 2: 'Do you remember if the consent contained the topic of sharing anonymous data with others?' ",
      "value": "1. Yes,\n0. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "mc_3",
      "description": "Manipulation check, question: 'Will your anonymous data be shared with others?'",
      "value": "1. Yes,\n0. No",
      "maxValue": 1,
      "minValue": 0,
      "@type": "propertyValue"
    },
    {
      "name": "remarks",
      "description": "Observer remarks",
      "@type": "propertyValue"
    },
    {
      "name": "Extraversion",
      "description": "8 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Agreeableness",
      "description": "10 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Conscientiousness",
      "description": "9 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Neuroticism",
      "description": "8 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Openness to experience",
      "description": "10 bf items aggregated by rowMeans",
      "@type": "propertyValue"
    },
    {
      "name": "Heteroduperie - Social Desirability",
      "description": "18 soc_d items aggregated by rowMeans",
      "@type": "propertyValue"
    }
  ]
}`